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Example review · fully redacted

See what you actually get before you buy.

This is a sample Full Snapshot for a made-up company. The business, the people, and the numbers are invented — but the structure, the tone, and the kind of findings are exactly what a real engagement delivers. Read it to decide whether this is the kind of clarity you want for your own workflow.

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Everything below is fictional and for illustration only. "Northwind Clinic Group" is not a real client. We never publish real client work.

Prepared for

Northwind Clinic Group (example)

Workflow reviewed

AI support assistant that drafts and auto-sends customer replies

Engagement

Full Snapshot

Turnaround

11 business days

The one-page summary

What we found, in plain English

Every review opens with a summary a busy owner can read in two minutes — before any detail.

Your support assistant is saving your team real time, and most of what it sends is fine. The problem is the small share that is not. Right now, replies about money and eligibility can reach a customer without anyone checking them, and when one is wrong, no single person is responsible for fixing it quickly. The "human review" step you set up is real, but the person doing it has far too much volume to actually catch mistakes.

None of this means you should turn the assistant off. It means three changes would remove most of the risk for very little effort: hold money-related replies for a human, give one person clear ownership and an off-switch, and lighten the reviewer's load so the check is real. We have listed these below, ordered by how soon they matter.

~1 in 6
auto-sent replies in our sample had a wrong policy detail
9 sec
median time to "review and approve" a flagged reply
3 changes
remove most of the risk — none require new software

The findings

What we looked at, and what we found

Each finding states what is happening, why it matters in real terms, the evidence behind it, and a specific fix. Nothing is a vague risk rating.

Act soon

The AI draft goes to the customer before a human sees it

What's happening
The support assistant writes refund and billing replies and sends them automatically when its confidence score is above a set threshold. Staff only review the ones it flags as low-confidence.
Why it matters
About 1 in 6 auto-sent replies in the sample contained a wrong policy detail (an amount, a date, or an eligibility rule). Because these never reach a person, the team only learns about them when a customer pushes back.
How we know
Reviewed 80 auto-sent replies from a two-week window and 3 customer complaint threads.
What to do
Hold any reply that mentions money, dates, or eligibility for a quick human check, regardless of confidence score. Keep auto-send for simple acknowledgements.
Act soon

Nobody owns the assistant when it goes wrong

What's happening
The tool was set up by a contractor who has since rolled off. Support, IT, and the vendor each assume one of the others is responsible for its behavior.
Why it matters
When the assistant sent an incorrect refund promise, it took two days to work out who could change the rule that caused it. During that time the same reply kept going out.
How we know
Interviews with the support lead, the IT manager, and a review of the vendor contract.
What to do
Name one internal owner who can pause the assistant and approve rule changes. Write down how to reach the vendor and what they are responsible for.
Plan for it

The "human review" step has quietly become a rubber stamp

What's happening
A reviewer is supposed to check flagged replies, but one person now handles roughly 200 a day alongside their normal work.
Why it matters
At that volume the reviewer can only skim. In the sample, flagged replies were approved in a median of 9 seconds — not enough time to actually verify them. The checkpoint exists on paper but is not adding much safety.
How we know
Reviewer queue timestamps over one week, plus a working session with the reviewer.
What to do
Either cut the volume reaching the reviewer (tighten what gets auto-handled) or add a second reviewer. A checkpoint nobody has time for is a false sense of security.
Plan for it

Customer data is leaving through the AI vendor without a clear record

What's happening
Full support tickets — including names, order details, and occasional payment fragments — are sent to the AI vendor to generate replies. There is no written record of what is shared or how long the vendor keeps it.
Why it matters
If a customer or an enterprise prospect asks "what happens to my data," the team cannot answer confidently today. This becomes a blocker the first time a larger customer runs a security review.
How we know
Sampled the data sent to the vendor API and reviewed the current (silent) data-handling terms.
What to do
Document what is shared, turn off any data retention the vendor allows you to disable, and remove payment fragments from ticket text before it is sent.
Good to know

No simple way to turn it off in a hurry

What's happening
There is no documented "stop" procedure. Disabling the assistant currently means finding the contractor-era admin login.
Why it matters
Not urgent today, but if the assistant starts misbehaving you want a known, fast way to switch it to draft-only or off — without a scramble.
How we know
Confirmed in the working session that no off-switch runbook exists.
What to do
Write a one-page "how to pause it" note and confirm the named owner can do it in under five minutes.

What we couldn't see

The open questions, named honestly

Every review says out loud where the evidence ran out. Unnamed uncertainty is more dangerous than uncertainty you can see.

We could not measure how often customers simply accept a wrong reply without complaining. Our error rate is from a sample; the real-world impact may be higher or lower.

The vendor would not confirm in writing how long they retain the ticket data sent to them. We have flagged this as a question to settle before any enterprise customer reviews you.

We reviewed two weeks of replies. Seasonal spikes (for example, end-of-year billing) were outside the window and may stress the workflow differently.

When to escalate

Clear triggers, written as things you'd actually notice

Not risk scores — observable conditions. If one of these happens, it stops being a normal support issue and goes to the named owner.

A customer says the assistant promised them money, a refund, or an outcome you did not authorize → pause auto-send and notify the owner the same day.

The same wrong reply goes out more than twice → it is a rule problem, not a one-off; the owner changes the rule before more go out.

A customer, partner, or prospect asks how their data is handled by the AI tool → route to the owner, do not improvise an answer.

The reviewer queue climbs past what one person can genuinely check → treat the human checkpoint as offline until volume is addressed.

The plan

What you'd do next, in order

A short, prioritized list sized to a small team — not a transformation program.

This week

Hold any reply mentioning money, dates, or eligibility for a human check. Name one owner who can pause the assistant.

This month

Lighten the reviewer load so the check is real. Document what data goes to the vendor and turn off retention you can disable.

Before you grow it

Write the one-page off-switch runbook and settle the vendor data question before any enterprise customer reviews you.

Where a fix is a repeatable pattern — like the data-handling step or the escalation triggers — we point you to the matching governance pack so you are not building the template from scratch.

Want this for your own workflow?

A $750 Workflow Snapshot gives you a shorter version of this.

The Workflow Snapshot covers one workflow and returns the top two or three things worth fixing first — in this same plain-language format. Start with one workflow ($750); go full-stack with a Full Snapshot when you're ready (from $3,500).

Start with a $750 Workflow Snapshot